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Comparison Dreame D9 Max vs Dreame L10 Pro

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Dreame D9 Max
Dreame L10 Pro
Dreame D9 MaxDreame L10 Pro
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Object recognition system using a stereoscopic camera. Lidar navigation. Dry and wet cleaning.
Typerobot vacuum cleanerrobot vacuum cleaner
Cleaning
dry and wet
side brush
turbo brush
wiping with a cloth
dry and wet
side brush
turbo brush
wiping with a cloth
Water supply adjustment
Robot vacuum cleaner
Suction power4000 Pa4000 Pa
Dust container capacity570 ml570 ml
Water tank capacity270 ml270 ml
Power auto-adjustment
Fine filterHEPAHEPA
Building a room maprangefinder (laser)rangefinder + camera
Cleaning area limitationvia the appvia the app
Features
Control via smartphone
Voice assistantAmazon AlexaAmazon Alexa
Scheduled cleaning
Object recognition
Battery
Battery capacity5.2 Ah5.2 Ah
Operating time150 min150 min
General
Cleaning area250 m²250 m²
Threshold clearance20 mm20 mm
Noise level65 dB65 dB
Dimensions (HxWxD)9.7x35x35 cm9.68x35.3x35 cm
Weight3.8 kg3.7 kg
Added to E-Catalogseptember 2021may 2021
Compare Dreame D9 Max and L10 Pro
Both robot vacuums, Dreame D9 Max and Dreame Bot L10 Pro, offer similar features, such as dry and wet cleaning, 4000 Pa suction power, and the ability to connect to a smartphone. However, the Bot L10 Pro stands out with its object recognition system using a stereoscopic camera and navigation with lidar, which can enhance its efficiency in complex spaces. Both devices have the same dustbin capacity (0.57 L) and water tank (0.27 L), and they can operate for up to 150 minutes on a single charge. In reviews, users note that both vacuums perform well in cleaning, but the L10 Pro may be preferable for more challenging tasks due to its improved navigation.
Dreame D9 Max often compared
Dreame L10 Pro often compared
Glossary

Building a room map

Map building allows the robot vacuum to remember the layout of the room, track covered areas, and plan a consistent cleaning route. The method used to create the map affects its accuracy, orientation speed, and the device’s ability to detect obstacles.

— With sensors. The robot creates an approximate map based on data from the gyroscope, motion sensors, wheels, and collisions with obstacles. This system is more affordable, but determines the exact position of walls and furniture less accurately, and accumulated errors may lead to repeated passes or missed areas.

— With a rangefinder. A laser rangefinder, or LiDAR, measures the distance to surrounding objects and creates an accurate map of the room before completing a full pass around it. It works reliably in low light, confidently detects walls and furniture, and supports room division and no-go zones.

— With a camera. The camera analyzes the surroundings and navigates using visual objects, helping refine the robot’s position and the room layout. Its advantage is the ability to distinguish individual objects, although performance depends more heavily on lighting.

— With a rangefinder and camera. The combined system uses LiDAR for accurate distance measurement and map building, and a camera to detect small objects on the floor. Such a robot not only navigates rooms confidently in any lighting, but also avoids cable...s, shoes, toys, and pet bowls more effectively.

Object recognition

A function in which the robot vacuum uses a camera and analysis algorithms to determine exactly what is on the floor. The device can distinguish cables, shoes, toys, pet bowls, and other small obstacles, then chooses a safe route around them. Compared to conventional sensors, object recognition does not simply detect an obstacle but identifies its type, reducing the risk of getting stuck, collisions, and cables wrapping around the brush.